In radiology, x-ray images are often viewed by the radiologists in pairs: an older image may be presented together with a newer image. Often, the newer and older images are acquired with different X-ray devices (modalities) and look quite different. In this case, the radiologist may have the task either “to look through” the modality dependent differences or to modify one of the images interactively as good as possible. Some differences always remain. In general, two separate systems are involved of generating X-ray images and displaying them to the radiologist. The first is the acquisition workstation AWS. Here, the images are processed and manipulated for optimal display. One step in this process is the ranging of the images. Here, the different histogram properties of images with different exposure settings or patient thickness are modified (semi and auto mode). The second modality is the diagnostic workstation. The general rule is to present the images here in a reproducible standardized way to the radiologist. Tools to modify brightness and contrast of the images are available for interactive use. In case, two images of the same patient but different modalities are presented, the appearance of the images might differ very much. A more similar viewing impression may be achieved only by interactive modifications of at least one of the images.
Furthermore, many X-ray devices are adapted for modifying brightness and contrast of images before displaying them. With some X-ray devices an interactive image adaptation process may be performed. With these devices, all adaptation is done manually with mouse actions on each image separately to achieve a closer match between two images. As a rule, the images are presented separately from each other.
Some X-ray devices are adapted for performing a so called “semi mode” and/or “auto mode”.
In the semi mode, from an image one relevant histogram key value is derived. For example, for a chest image it may be the average signal within the lungs. Then a look-up table may be generated that maps this key value to a preferred grey-value on the monitor or the printed film. The other parameters of the mapping curve may be pre-defined, such as the “contrast”. In a chest image the shown grey-value of a bright pixel in the abdomen will vary around a typical value for slim and heavy patients. The simplest definition of the key value may be a specific percentile value of the histogram.
In the auto mode, two key values are derived from the histogram of an image. One represents a dark part of the image (such as the lungs) and another one a bright part, such as the abdomen. The look-up table may be generated in a way that two pre-define grey-values are matched at the end, a proper one for each key value. This may lead to a more stable image display also with strongly varying histograms of slim and heavy patients. The simplest definition of the two key values may be a low and high percentiles value of the histogram.
Normally, images are modified separately from each other, for example see WO 2010/020921 A2.
In “Interactive Multi-contrast Enhancement of Previously Processed Digital Mammograms” (International Workshop on Digital Mammography, Jun. 16-18, 2010, Girona, Spain; Fabian Zöhrer et al., ISBN 978-3-642-13665-8) a method is described in which two histograms of two images are mapped for adapting one of the images to the other.